Next Article in Journal
The Role of Manager Compensation and Integrated Reporting in Company Value: Indonesia vs. Singapore
Previous Article in Journal
Social Impacts of a Mega-Dam Project as Perceived by Local, Resettled and Displaced Communities: A Case Study of Merowe Dam, Sudan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Economies
Volume 9
Issue 4
10.3390/economies9040141
economies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Different View on ASEAN Capital Market Integration

by
Robiyanto Robiyanto
1,*,
Budi Frensidy
2,
Ignatius Roni Setyawan
3 and
Andrian Dolfriandra Huruta
1
1
Faculty of Economics and Business, Satya Wacana Christian University, Salatiga 50711, Indonesia
2
Faculty of Economics and Business, Universitas Indonesia, Depok 16424, Indonesia
3
Faculty of Economics and Business, Tarumanagara University, Jakarta 11440, Indonesia
*
Author to whom correspondence should be addressed.
Economies 2021, 9(4), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/economies9040141
Submission received: 28 July 2021 / Revised: 18 August 2021 / Accepted: 30 August 2021 / Published: 1 October 2021
Browse Figure
Review Reports Versions Notes

Abstract

:
Capital market integration has become an interesting research topic nowadays. Many studies have tried to explain this phenomenon using various methods. Here, we used sophisticated methods to explain capital market integration. This study aims to scrutinize the Association of Southeast Asian Nations (ASEAN) capital market integration. This study hopefully can enrich the different views regarding the capital market integration and fill the gap left by previous studies. The data used were the stock price index of the monthly closing data of the capital markets in ASEAN countries during the period of January 1999 to December 2020, obtained from Bloomberg and the Indonesia Stock Exchange. Data in this study were analyzed using the wavelet method. The results indicate that there is a long-term integration in the capital markets of ASEAN countries, and the highest level of integration was in the period during and about a year after the global crisis. Using the spectrum analysis, the results show that during period from 2008 to 2010, the level of integration reached its highest level.

1. Introduction

Capital market integration is an interesting topic in the finance field and is widely discussed by many researchers. Capital market integration always moves dynamically along in time for several reasons. For example, due to increasingly advanced technology, investors can easily gain access and exchange information from one capital market to another; and the thinner the boundaries in the global financial sector, the flow of money can easily go from one country to another (Baele et al. 2004). The current capital market in Indonesia also tends to be integrated with other capital markets. There are many reasons for this, one of them was the removal of restrictions on foreign investor ownership of a public company in 1999 (Bilson et al. 2001; Muharam 1999; Suryanta 2011).
Various studies examining the integration of the Indonesian capital market with other capital markets have also been carried out, for example Palac-McMiken (1997), who conducted a study of the integration of several capital markets in ASEAN countries, such as the Jakarta Stock Exchange, Singapore Stock Exchange, Kuala Lumpur Stock Exchange, Philippines Stock Exchange, and Stock Exchange Thailand in the period before the Asian financial crisis. This study found that only the Indonesian capital market was not integrated with the other capital markets. This finding is also supported by Roca et al. (1998). In the period after the Asian financial crisis, Suryanta (2011) found consistencies with the findings of Palac-McMiken (1997), Kim and McKenzie (2008), and Roca et al. (1998). However, Robiyanto and Ernayani (2018) found relatively different results, where after the Asian financial crisis, the Indonesian capital market actually had the same movement (co-movement) as the capital market in ASEAN countries. Meanwhile, Muharam et al. (2020) measured the degree of integration of capital markets in Asia using the Orthogonal GARCH approach, and found that the Indonesian, Philippine, and Malaysian capital markets were the capital markets that were segmented and were not integrated with the capital markets in Asia.
Based on this explanation, there is a diversity of research results related to the integration of the Indonesian capital market with capital markets in Asian countries. The variety of previous studies’ results provide a gap to be solved, especially by using more sophisticated approaches to enrich the different views regarding capital market integration. There is a relatively strong analytical tool for predicting capital market integration—the wavelet analysis. The wavelet approach allows researchers to examine the behavior of a time series along with its frequency and space−time (Mensi et al. 2018; Power 2007). This method has been used by Das et al. (2018) to examine the co-movement between gold and stock prices in ASEAN countries, and by Mensi et al. (2018) to examine the co-movement between gold and stock prices in BRICs countries. Unfortunately, this method is still relatively rarely used for predicting the level of capital market integration (only a few using this method, such as Ranta (2013) in studying the integration of several leading capital markets), although it is considered to have the ability to better explain the level of capital market integration (Das et al. 2018; Ranta 2013).
This study uses the wavelet analysis to examine the integration of capital markets in ASEAN countries from January 1999 to December 2020. The wavelet analysis is performed using wavelet multiple correlation (WMC) and the spectrum analysis. The wavelet approach is presumed to be suitable for the capital integration analysis. By using this analysis, this study can hopefully fill some of the gaps of previous studies such as Palac-McMiken (1997), Kim and McKenzie (2008), Muharam et al. (2020), and Roca et al. (1998). The result of this study shows that long-term integration in the capital markets of ASEAN countries still exist, while the highest level of integration was in the period during and about a year after the global crisis (2008–2009). Hopefully, this study can sharpen the previous studies’ findings regarding ASEAN capital market integration and can help researchers, practitioners, and authorities gain more insight into and the ability to formulate policies to cope with the issues examined.

2. Literature Review

2.1. Capital Market Integration

A general definition of the “financially integrated market” that is regularly used is the definition expressed by Baele et al. (2004), Weber (2006), and Chen and Knez (1995). They suggest the market for a financial instrument and service is fully integrated if all potential parties participating in the market share the same characteristics when confronting a single set of rules. For instance, when participants are willing to make a transaction of certain financial instruments/services, have access to all of the financial instruments/services and receive the same treatment. This is very likely to happen for capital markets in the ASEAN (Association of Southeast Asian Nations) region, because the ASEAN Economic Community (AEC) has been implemented (Jarungkitkul and Sukcharoensin 2016; Noman et al. 2017; Robiyanto et al. 2016; Volz 2013). The implementation of the AEC would hypothetically improve ASEAN capital market integration (Gugler and Vanoli 2017; Robiyanto et al. 2016) and economic cooperation (Gugler and Vanoli 2017).
Consistent with Baele et al. (2004), Weber (2006), and Karolyi and Stulz (1995); Valle (2000) suggest that the capital market can experience the same movement (co-movement) because of the underlying economic factors that reflect the financial conditions in the world in general, and systematically affect all markets. Deregulation and market liberalization, developments in communication technology and trading systems, innovation in financial products and services, and the increasing international activity of multinational companies also affect the capital market co-movement in various countries (Chay and Eleswarapu 2001).
Jorion and Schwartz (1986) also argued that the capital market is considered internationally integrated whenever assets with a similar risk are also traded with a similar price, despite being traded in various capital markets. This means that the risk and return apply internationally and the stock movements in the capital market are not only influenced by domestic factors, but by stock price movements in world capital markets (Kouki et al. 2011; Maghyereh et al. 2017; Pradhan et al. 2020; Qadan et al. 2019; Qarni et al. 2019; Robiyanto et al. 2019; Xu et al. 2019). In addition, Bekaert et al. (2007) stated that in a market that has a high level of integration and a contagion effect, it will move together and have a high level of association. Meanwhile, the opposite of an integrated capital market is segmented capital market. A capital market can be considered segmented if it has a low relationship with other stock markets (Bilson et al. 2001).

2.2. Capital Market Integration in ASEAN

Palac-McMiken (1997) conducted research on the integration of several capital markets in ASEAN countries, such as the Philippines Stock Exchange, Jakarta Stock Exchange, Singapore Stock Exchange, Kuala Lumpur Stock Exchange, and Stock Exchange Thailand, during the period of January 1987 to October 1995. By using a cointegration analysis, the results showed that all of the studied capital markets except for the Jakarta (Indonesia) Stock Exchange were related to each other. This study also found that the capital market in the period of 1987–1995 was a collectively inefficient market.
Roca et al. (1998) found results consistent with Palac-McMiken (1997). Roca et al. (1998) examined the relationship between several capital markets in ASEAN countries, similar to those studied previously during the period of 1988–1995. The results indicated that the Jakarta (Indonesia) Stock Exchange was not connected at all with the capital markets in other ASEAN countries, both in the long and short term. Furthermore, this study concluded that in the short term, all capital markets—except the Jakarta Stock Exchange—had a relationship with each other. Meanwhile, in the long term, all of the capital markets studied did not have any relationship between them.
Ibrahim (2006) conducted a study on the same capital markets using the research period from January 1988 to December 2003, but the analytical tool that was used was vector autoregressive (VAR). This study examined the influence of developed capital markets such as the United States capital market and the Japan capital market on ASEAN capital markets. It found that the United States capital market had a more dominant influence than the Japan capital market on the ASEAN capital markets. The existing empirical evidence also shows that the ASEAN capital markets are very vulnerable to international financial crises. This could be seen from the significant influence of the turmoil that occurred in the United States capital market, while the drastic positive changes in the developed capital markets did not have any significant impact.
Using more recent data that were also processed with VAR, Suryanta (2011) conducted research on capital market integration in several ASEAN countries such as for Indonesia, Malaysia, Singapore, Thailand, and Philippines. The data used in this study were from January 2004 to December 2009. The results were consistent with Palac-McMiken (1997), Click and Plummer (2005), and Roca et al. (1998), revealing that there was no co-movement and dynamic relationship between the Indonesian capital market and the Singapore, Thailand, Malaysia, and the Philippines capital markets. Furthermore, Suryanta (2011) also suggested that Indonesia’s domestic factors influenced the Indonesian capital market more than the external factors.
Furthermore, Karim and Karim (2012) conducted more detailed research on the capital market integration in several ASEAN countries, such as Malaysia, Indonesia, Singapore, Thailand, and the Philippines, using the data from January 1988 to December 2010 that were divided into the periods before and after 1997, as well as the period after the financial crisis (sub-prime mortgage) in United States. Karim and Karim (2012) concluded that the capital markets in ASEAN countries tended to be integrated, especially after the sub-prime mortgage crisis in the United States. This is consistent with the results of Robiyanto and Ernayani (2018), and Catherine and Robiyanto (2020), who used vector autoregression (VAR), and Robiyanto (2018), who used the DCC-GARCH method for investigating the integration of the Indonesian capital market with ASEAN and Asian countries, and found that the dynamic correlation of the Indonesian capital market with ASEAN countries tended to strengthen over time after the Asian financial crisis. These results were also consistent with those found by Muharam et al. (2020), who used the Orthogonal GARCH (OGARCH) method and found that the highest level of integration was found during the global financial crisis.

3. Method

3.1. Data

This study uses the stock price index for the monthly closing data of ASEAN capital markets, such as the Jakarta Composite Index (JCI) from the Indonesia Stock Exchange (IDX), the Strait Times Index (STI) from the Singapore Stock Exchange (SSX), the Kuala Lumpur Composite Index (KLCI) from Kuala Lumpur Stock Exchange (KLSE), the SET Index (SETi) from the Stock Exchange Thailand (SET), and the Philippines Stock Exchange Index (PSEi) from the Philippines Stock Exchange (PSE) during the period of January 1999 to December 2020. The monthly closing of the stock index data for JCI, STI, KLCI, SETi, and PSEi were obtained from Bloomberg.

3.2. Analysis Technique

This research used the wavelet analysis. For analyzing, encoding, compressing, reconstructing, and modeling the signals and images, wavelet analysis provides extremely strong problem-solving tools. MATLAB software was used to create many wavelet packages. Continuous wavelet transforms (CWT), and 1D and 2D discrete wavelet transforms (DWT) are all included in the wavelet toolbox. Mensi et al. (2018) and Power (2007) suggested that the wavelet analysis allows researchers to examine the behavior of a time series along with frequency and space−time. Das et al. (2018) explained that the wavelet correlation is an interaction between wavelet covariance [ x ( t ) ,   y ( t ) ] and wavelet variance for [ x ( t ) ] and [ y ( t ) ] , where x ( t ) and y ( t ) are the stock indexes’ time series. For the continuous transformations of W x ( r , s ) and W y ( r , s ) , W x y ( r , s ) = W x ( r , s ) W y * ( r , s ) , where r denotes the position index and s is the scale. The asterisk (*) denotes the complex conjugate. Furthermore, the wavelet coherence analysis (WCA) is one of the most frequently used forms of wavelet analysis used to measure the co-movement of two different markets using the space and time dimensions (Jena et al. 2018; Papiol 2018).
Antonakakis et al. (2018) suggested that the wavelet analysis could show information about the time and frequency series data. It could also be applied to non-stationary datasets. This study used the estimation of the wavelet coherence introduced by Li et al. (2015) of the cross-wavelet and auto-wavelet power spectrum, as follows:
R x y 2 ( r , s ) = | S ( s − 1 W x y ( r , s ) | 2 S ( s − 1 | ( W x ( r , s ) | 2 ) S ( s − 1 | ( W y ( r , s ) | 2
R x y 2 ( r , s ) is the wavelet coherency. S is the smoothing operator. This formula gives a value between 0 to 1 in the time frequency window. Zero coherence indicates that there is no co-movement between the existing returns and currents, while the highest coherence implies there is the strongest co-movement between the two datasets. In the wavelet coherence plot, the red color indicates a strong co-movement, while the blue color indicates a weak co-movement. As it is not easy to distinguish positive and negative co-movements as the wavelet coherence is squared, this study uses a phase difference to provide information about the negative and positive co-movements as an indicator of the relationship between two data sets, as used by Antonakakis et al. (2018). Bloomfield et al. (2004) formulated the phase difference relationship between x ( t ) and y ( t ) as follows:
φ x y = t a n − 1 ( J { S ( s − 1 W x y ( r , s ) ) } R { S ( s − 1 W x y ( r , s ) ) } ) ,   with   φ x y ∈ [ − ∏ , ∏ ] ,
J and R are equal to the imaginary transformation and the real part of the smoothed cross wavelet, respectively. Wavelet analysis in this study was conducted using MATLAB software.

4. Results and Discussion

In order to understand the nature of the data used, descriptive statistics for the ASEAN stock price indexes studied are shown in Table 1. Table 1 shows that JCI’s mean was higher than for the other ASEAN’s stock markets, while the riskiest stock market was Stock Exchange Thailand with the highest standard deviation.
The wavelet analysis is a sophisticated analysis that needs some pre-testing steps. Before performing the wavelet analysis, the unit root test was first performed with an augmented Dickey−Fuller statistical test and Johansen cointegration analysis. Kumar and Ajaz (2019), and Robiyanto and Ernayani (2018) stated that this was done for the long-term cointegration of the capital market under study. The results of the unit root test can be seen in Table 2.
Based on Table 2, it can be seen that all of the augmented Dickey−Fuller tests were statistically significant at a significance level of 1%. This indicates that there was no unit root found in the variables used. The next stage was to perform the Johansen cointegration analysis in order to test whether a long-term cointegration existed. The results of the Johansen cointegration analysis can be seen in Table 3. The Johansen cointegration shows that there was a cointegration in the ASEAN capital markets studied (the probability value was less than 0.05), so that the wavelet analysis could be carried out. Wavelet coherency provided a much better measure of co-movement between the stock market returns. The results reveal that there was a cointegration of ASEAN capital markets studied at all of the existing levels. This is consistent with the findings of Seth and Sharma (2015), who found a long-term cointegration in Asian capital markets.
Based on the wavelet analysis, Figure 1 displays the WMC scalogram and spectrum analyses of the ASEAN capital markets studied during the period of 1999 to 2020. This WMC value ranged from −0.3 to 0.35, which indicates that, in general, the level of integration between the capital markets in ASEAN regions tends to be low.
The wavelet analysis could produce more precise results regarding the level of integration. In the period after the Asian crisis until the period of the global financial crisis in 2008, the level of capital market integration in ASEAN region is seen to be higher and more fluctuating than the period after the global financial crisis. This is different from the results of previous studies (Karim et al. 2011; Muharam et al. 2020; Robiyanto 2018), who generally found that the level of capital market integration in ASEAN before the global financial crisis tended to be lower than after the global financial crisis, because the speed of information transmission was faster in the period after the global financial crisis. This is in line with Horobet and Lupu (2009), who concluded that the speed of transmission could boost the integration.
However, based on the spectrum analysis conducted in this study, the volatility among stock markets in the ASEAN region tend to be higher and more fluctuated. This could be because the Asian Crisis produced some uncertainty, including political uncertainty and social unrest, in some ASEAN countries. Again, by using the spectrum analysis, it can be seen that during the research period, the highest and most striking level of integration for the overall capital market in the ASEAN region was in 2008 to 2010, during the period of the global financial crisis and about one year thereafter. It is known that the global financial crisis created extreme fear due to the derivative (such as collateralized debt obligation (CDO) and collateralized mortgage obligation (CMO)), which led to the sub-prime mortgage crisis in 2008, later known as the global financial crisis (GFC), a severe worldwide crisis in financial sector often referred to as a black swan event (Bekiros et al. 2017; Estrada and Varga 2012). So, it is not surprising that short term shocks and some uncertainty were created in the one-year aftermath.
The result shows that during the period of the GFC (Majid and Kassim 2009; Karim et al. 2010) between July 2007 to December 2008, the peak was reached in September 2008 when Lehman-Brothers collapsed, and about one year after the global financial crisis the ASEAN capital markets tended to have the same direction of movement due to the global uncertainty as a result of the global financial crisis. In this context, the results of this study are able to support the research results by Muharam et al. (2020), who concluded that in the period of the global financial crisis, the highest level of capital market integration in the ASEAN region was found during the global financial crisis compared with the periods before and after.

5. Conclusions

This study uses wavelet analysis to examine the integration of capital markets in the ASEAN region through wavelet multiple correlation (WMC) and the spectrum analysis. We used the unit root test and Johansen cointegration analysis before conducting the wavelet analysis. Based on those analyses, the wavelet analysis was appropriate for use in this study. The results of this study indicate that the ASEAN capital market integration during the period of 1999 to 2020 had a low correlation trend and tended to lack intensity. By using a more detail analysis, the highest level of integration occurred in the period from 2008 to 2010, which was during the period of the global financial crisis and the year following it. This present study has more detailed results compared with the ones in previous studies, because it directly found the strongest level of integration using the spectrum analysis. The findings imply that the ASEAN capital markets are less integrated, and this condition will beneficial global portfolio formulation. However, during a crisis, due to higher degree of integration, the global portfolio formulation would have less benefit.
The results of the spectrum analysis specifically indicate that the period with the most striking level of integration is September 2008 (when Lehman-Brothers collapsed) to December 2008; unfortunately, this study still uses the monthly data from the research period. Therefore, it is suggested that future uses daily data to obtain more detailed results and adds several variants of the wavelet analysis. Other approaches, such as the ADCC-GARCH and other GARCH variants, are also applicable.

Author Contributions

Conceptualization, R.R., B.F. and I.R.S.; methodology, R.R., B.F., I.R.S. and A.D.H.; software, R.R. and A.D.H.; validation, R.R., B.F., I.R.S. and A.D.H.; formal analysis, R.R., B.F., I.R.S. and A.D.H.; investigation, R.R., B.F. and I.R.S.; resources, R.R., B.F., I.R.S. and A.D.H.; data curation, R.R., A.D.H., and B.F.; writing—original draft preparation, R.R., B.F., I.R.S. and A.D.H.; project administration, I.R.S. and A.D.H.; funding acquisition, R.R., B.F. and I.R.S.; writing—review and editing, R.R., B.F., I.R.S. and A.D.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Direktorat Riset dan Pengabdian Masyarakat, Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi (Directorate of Research and Community Service, Ministry of Education, Culture, Research, and Technology), Indonesia. Grant Number: 166/E4.1/AK.04.PT/2021. The APC was funded by Direktorat Riset dan Pengabdian Masyarakat, Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi (Directorate of Research and Community Service, Ministry of Education, Culture, Research and Technology), Indonesia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Antonakakis, Nikolaos, Tsangyao Chang, Juncal Cunado, and Rangan Gupta. 2018. The relationship between commodity markets and commodity mutual funds: A wavelet-based analysis. Finance Research Letters 24: 1–9. [Google Scholar] [CrossRef] [Green Version]
  2. Baele, Lieven, Annalisa Ferrando, Peter Hordahl, Elizaveta Krylova, and Cyril Monnet. 2004. Measuring Financial Integration in The Euro Area. European Central Bank Occasional Paper Series 14: 1–95. [Google Scholar]
  3. Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad. 2007. Global Growth Opportunities and Market Integration. Journal of Finance 62: 1081–137. [Google Scholar] [CrossRef]
  4. Bekiros, Stelios, Sabri Boubaker, Duc Khuong Nguyen, and Gazi Salah Uddine. 2017. Black swan events and safe havens: The role of gold in globally integrated emerging markets. Journal of International Money and Finance 73: 317–34. [Google Scholar] [CrossRef] [Green Version]
  5. Bilson, Christopher, Vince Hooper, and Martin Jaugietis. 2001. The Impact of Liberalization and Regionalism upon Capital Markets in Emerging Asian Economics. In Asian Financial Crisis Financial, Structural and International Dimensions (International Finance Review). Edited by Jongmoo Jay Choi. Bingley: Emerald Group Publishing Limited, pp. 199–235. [Google Scholar]
  6. Bloomfield, D. Shaun, R. T. James McAteer, Bruce W. Lites, Philip G. Judge, Mihalis Mathioudakis, and Francis P. Keenan. 2004. Wavelet Phase Coherence Analysis: Application to a Quiet-Sun Magnetic Element. The Astrophysical Journal 617. [Google Scholar] [CrossRef] [Green Version]
  7. Catherine, Happy, and Robiyanto Robiyanto. 2020. Capital Market Integration in Asian and United States Region After Donald Trump’s Inauguration. Jurnal Ilmiah MEA (Manajemen, Ekonomi, & Akuntansi) 4: 1–17. [Google Scholar] [CrossRef]
  8. Chay, Jong-Bom, and Venkat R. Eleswarapu. 2001. Deregulation and Capital Market Integration: A Study of the New Zealand Stock Market. Pacific-Basin Finance Journal 9: 29–46. [Google Scholar] [CrossRef]
  9. Chen, Zhiwu, and Peter J. Knez. 1995. Measurement of Market Integration and Arbitrage. Review of Financial Studies 8: 287–325. [Google Scholar] [CrossRef]
  10. Click, Reid W., and Michael G. Plummer. 2005. Stock Market Integration in ASEAN after the Asian Financial Crisis. Journal of Asian Economics 16: 5–28. [Google Scholar] [CrossRef]
  11. Das, Debojyoti, Manoharan Kannadhasan, Khamis Hamed Al-Yahyaee, and Seong-Min Yoon. 2018. A wavelet analysis of co-movements in Asian gold markets. Physica A: Statistical Mechanics and its Applications 492: 192–206. [Google Scholar] [CrossRef]
  12. Estrada, Javis, and Maria Varga. 2012. Black Swan, Beta, Risk, and Return. Journal of Applied Finance 22: 77–89. [Google Scholar] [CrossRef] [Green Version]
  13. Gugler, Philippe, and Laura Vanoli. 2017. Convergence or divergence of prosperity within the ASEAN community? A crucial issue for the success of the ASEAN economic community (AEC) process. International Journal of Emerging Markets 12: 610–24. [Google Scholar] [CrossRef]
  14. Horobet, Alexandra, and Radu Lupu. 2009. Are Capital Markets Integrated? A Test of Information Transmission Within The European Union. Romanian Journal of Economic Forecasting 2: 64–80. [Google Scholar]
  15. Ibrahim, Mansoor H. 2006. International Linkage of ASEAN Stock Prices: An Analysis of Response Asymmetries. Applied Econometrics and International Development 3: 191–202. [Google Scholar]
  16. Jarungkitkul, Wanida, and Sorasart Sukcharoensin. 2016. Benchmarking the competitiveness of the ASEAN 5 equity markets. Benchmarking: An International Journal 23: 1312–40. [Google Scholar] [CrossRef]
  17. Jena, Sangram Keshari, Aviral Kumar Tiwari, and David Roubaud. 2018. Comovements of gold futures markets and the spot market: A wavelet analysis. Finance Research Letters 24: 19–24. [Google Scholar] [CrossRef]
  18. Jorion, Philippe, and Eduardo Schwartz. 1986. Integration vs. Segmentation in the Canadian Stock Market. Journal of Finance 41: 603–14. [Google Scholar] [CrossRef]
  19. Karim, Bakri Abdul, and Zulkepfly Abdul Karim. 2012. Integration of ASEAN-5 Stock Markets: A Revisit. Asian Academyof Management Journal of Accounting and Finance 8: 21–41. [Google Scholar]
  20. Karim, Bakri Abdul, Nor Akila Mohd Kassim, and Mohammad Affendy Arip. 2010. The Subprime Crisis and Islamic Stock Markets Integration. International Journal of Islamic and Middle Eastern Finance and Management 3: 363–71. [Google Scholar] [CrossRef]
  21. Karim, Bakri Abdul, Mohamad Jais, and Samsul Ariffin Abdul Karim. 2011. The Subprime Crisis and Stock Index Futures Markets Integration. The Journal of Risk Finance 12: 400–8. [Google Scholar] [CrossRef]
  22. Karolyi, G. Andrew, and Rene M. Stulz. 1995. Why do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovement Using ADRS. SSRN Working Paper W8538: 1–38. [Google Scholar] [CrossRef] [Green Version]
  23. Kim, Suk-Joong, and Michael D. McKenzie. 2008. Autocorrelation and Stock Market Integration in the Asia-Pacific. In Asia-Pacific Financial Markets: Integration, Innovation and Challenges. Amsterdam: Elsevier, Ltd. [Google Scholar]
  24. Kouki, Imen, Nizar Harrathi, and Mahfuzul Haque. 2011. A Volatility Spillover among Sector Index of International Stock Markets. Journal of Money, Investment and Banking 22: 32–45. [Google Scholar]
  25. Kumar, Anoop S., and Taufeeq Ajaz. 2019. Co-movement in crypto-currency markets: Evidences from wavelet analysis. Financial Innovation 5. [Google Scholar] [CrossRef] [Green Version]
  26. Li, Xiao-Lin, Tsangyao Chang, Stephen M. Miller, Mehmet Balcilar, and Rangan Gupta. 2015. The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains. International Review of Economics & Finance 38: 220–33. [Google Scholar] [CrossRef] [Green Version]
  27. MacKinnon, James G., Alfred A. Haug, and Leo Michelis. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics 15: 567–77. [Google Scholar] [CrossRef]
  28. Maghyereh, Aktham I., Basel Awartani, and Panagiotis Tziogkidis. 2017. Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries. Energy Economics 68: 440–53. [Google Scholar] [CrossRef] [Green Version]
  29. Majid, M. Shabri Abd, and Salina Hj Kassim. 2009. Impact of the 2007 US financial crisis on the emerging equity markets. International Journal of Emerging Markets 4: 341–57. [Google Scholar] [CrossRef]
  30. Mensi, Walid, Besma Hkiri, Khamis H. Al-Yahyaee, and Sang Hoon Kang. 2018. Analyzing time–frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics & Finance 54: 74–102. [Google Scholar] [CrossRef]
  31. Muharam, Harjum. 1999. Dampak Pelepasan Batas Kepemilikan Asing Terhadap Terintegrasinya Bursa Efek Jakarta dengan Bursa Efek Internasional (Studi Literatur). Jurnal Bisnis Strategi 4: 1–7. [Google Scholar]
  32. Muharam, Harjum, Robiyanto Robiyanto, Irene Rini Demi Pangestuti, and Wisnu Mawardi. 2020. Measuring Asian Stock Market Integration by Using Orthogonal Generalized Autoregressive Conditional Heteroscedasticity. Montenegrin Journal of Economics 16: 121–37. [Google Scholar] [CrossRef]
  33. Noman, Abu Hanifa Md, Chan Sok Gee, and Che Ruhana Isa. 2017. Does competition improve financial stability of the banking sector in ASEAN countries? An empirical analysis. PLoS ONE 12: e0176546. [Google Scholar] [CrossRef]
  34. Palac-McMiken, Evanor D. 1997. An Examination of ASEAN Stock Markets: A Cointegration Approach. ASEAN Economic Bulletin 13: 299–311. [Google Scholar] [CrossRef]
  35. Papiol, Gemma Colldeforns. 2018. Wavelet Approach in Computational Finance. Barcelona: Universitat Autonoma de Barcelona. [Google Scholar]
  36. Power, Gabriel. 2007. A Wavelet-Based Analysis of Commodities Futures Markets. Ithaca: Cornell University. [Google Scholar]
  37. Pradhan, Rudra P., Mak B. Arvin, Neville R. Norman, and Sahar Bahmani. 2020. The dynamics of bond market development, stock market development and economic growth. Journal of Economics, Finance and Administrative Science 25: 119–47. [Google Scholar] [CrossRef]
  38. Qadan, Mahmoud, Doron Kliger, and Nir Chen. 2019. Idiosyncratic volatility, the VIX and stock returns. The North American Journal of Economics and Finance 47: 431–41. [Google Scholar] [CrossRef]
  39. Qarni, Muhammad Owais, Saqib Gulzar, Syeda Tamkeen Fatima, Majid Jamal Khan, and Khurram Shafi. 2019. Inter-Markets Volatility Spillover in U.S. Bitcoin and Financial Markets. Journal of Business Economics and Management 20: 694–714. [Google Scholar] [CrossRef] [Green Version]
  40. Ranta, Mikko. 2013. Contagion among major world markets: A wavelet approach. International Journal of Managerial Finance 9: 133–49. [Google Scholar] [CrossRef]
  41. Robiyanto, Robiyanto. 2018. Indonesian Stock Market’s Dynamic Integration with Asian Stock Markets and World Stock Markets. Jurnal Pengurusan 52: 181–92. [Google Scholar] [CrossRef] [Green Version]
  42. Robiyanto, Robiyanto, and Rihfenti Ernayani. 2018. Capital market integration in some ASEAN countries revisited. Jurnal Manajemen 22: 205–22. [Google Scholar] [CrossRef] [Green Version]
  43. Robiyanto, Robiyanto, Hersugondo Hersugondo, and Gatyt Sari Chotijah. 2016. ASEAN Economic Community (AEC) and Economic Stability: A Review from Indonesia’s Side. International Journal of Economic Research 13: 603–11. [Google Scholar]
  44. Robiyanto, Robiyanto, Michael Santoso, Apriani Dorkas Rambu Atahau, and Harijono Harijono. 2019. The Indonesia Stock Exchange and Its Dynamics: An Analysis of the Effect of Macroeconomic Variables. Montenegrin Journal of Economics 15: 59–73. [Google Scholar] [CrossRef]
  45. Roca, Eduardo D., Eliyathamby Antony Selvanathan, and William F. Shepherd. 1998. Are the ASEAN Equity Markets Interdependent? ASEAN Economic Bulletin 15: 109–20. [Google Scholar] [CrossRef]
  46. Seth, Neha, and Anil K. Sharma. 2015. International Stock Market Efficiency and Integration: Evidences from Asian and US Markets. Journal of Advances in Management Research 12: 88–106. [Google Scholar] [CrossRef]
  47. Suryanta, Barli. 2011. Capital Market Integration in ASEAN Countries: Special Investigation of Indonesian Towards the Big Four. Asian Journal of Technology Management 4: 109–14. [Google Scholar]
  48. Valle, Rene Sanchez. 2000. A Cointegration Analysis of Latin American Stock Markets and The U.S. SSRN Working Paper. [Google Scholar]
  49. Volz, Ulrich. 2013. ASEAN Financial Integration in the Light of Recent European Experiences. Journal of Southeast Asian Economics 30: 124–42. [Google Scholar] [CrossRef] [Green Version]
  50. Weber, Axel A. 2006. European Financial Integration and (its Implications for) Monetary Policy. In Annual General Meeting 2006 of the Foreign Bankers’ Association in the Netherlands. Amsterdam: Deutsche Bundesbank. [Google Scholar]
  51. Xu, Weiju, Feng Ma, Wang Chen, and Bing Zhang. 2019. Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States. Energy Economics 80: 310–20. [Google Scholar] [CrossRef]
Economies 09 00141 g001 550
Figure 1. Scalogram and spectrum analysis.
Figure 1. Scalogram and spectrum analysis.
Economies 09 00141 g001
Table
Table 1. Augmented Dickey−Fuller test statistics.
Table 1. Augmented Dickey−Fuller test statistics.
DescriptionJCISTIKLCISETiPSEi
Mean0.01228 0.00429 0.005140.007560.00666
Max0.25813 0.24224 0.342280.304830.19993
Min−0.31422−0.23939−0.18628−0.30176−0.24072
Stdev0.06452 0.05694 0.050980.067420.05881
Source: Bloomberg, processed.
Table
Table 2. Augmented Dickey−Fuller test statistics.
Table 2. Augmented Dickey−Fuller test statistics.
No.VariableLevel **
1.Jakarta Composite Index (JCI)−12.87381 *
2.Straits Times Index (STI)−15.26061 *
3.Stock Exchange of Thailand Index (SETi)−15.58269 *
4.Philippine Stock Exchange Index (PSEi)−14.90462 *
5.Kuala Lumpur Composite Index (KLCI)−15.46599 *
* level of significance at the 1%; ** level refers to the order of integration in unit root test. Note: None unit root found.
Table
Table 3. Results of the Johansen cointegration analysis.
Table 3. Results of the Johansen cointegration analysis.
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)		EigenvalueMax-Eigen
Statistic		0.05 Critical ValueProb.**
None0.28285386.1108833.876870.0000
At most 1 *0.23466069.2657927.584340.0000
At most 2 *0.19303455.5487321.131620.0000
At most 3 *0.15709644.2636014.264600.0000
At most 4 *0.12776635.404673.8414660.0000
Max-eigenvalue test indicates 5 cointegrating equation(s) at the 0.05 level; * denotes rejection of the hypothesis at the 0.05 level; ** MacKinnon et al. (1999) p-values.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Robiyanto, R.; Frensidy, B.; Setyawan, I.R.; Huruta, A.D. A Different View on ASEAN Capital Market Integration. Economies 2021, 9, 141. https://0-doi-org.brum.beds.ac.uk/10.3390/economies9040141

AMA Style

Robiyanto R, Frensidy B, Setyawan IR, Huruta AD. A Different View on ASEAN Capital Market Integration. Economies. 2021; 9(4):141. https://0-doi-org.brum.beds.ac.uk/10.3390/economies9040141

Chicago/Turabian Style

Robiyanto, Robiyanto, Budi Frensidy, Ignatius Roni Setyawan, and Andrian Dolfriandra Huruta. 2021. "A Different View on ASEAN Capital Market Integration" Economies 9, no. 4: 141. https://0-doi-org.brum.beds.ac.uk/10.3390/economies9040141

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop